code: def step(self, action_dict):
      assert len(action_dict) == 1, action_dict
      if "high_level_agent" in action_dict:
          return self._high_level_step(action_dict["high_level_agent"])
      else:
          return self._low_level_step(list(action_dict.values())[0])
  
file_path: ray/rllib/examples/env/windy_maze_env.py
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete, Tuple
  import logging
  import random
  from ray.rllib.env import MultiAgentEnv
label_desc: Use image node_id 3 for calling the HierarchicalWindyMazeEnv obj's underlying member method code with example usage: obj.step(action_dict) and returns: self, self